Model-based co-clustering for functional data
Résumé
As a consequence of the recent policies for smart meter development, electricity operators are nowadays able to collect data on electricity consumption widely and with a high frequency. This is in particular the case in France where EDF will be able soon to remotely record the consumption of its 27 millions clients every 30 minutes. We propose a new co-clustering methodology, based on the functional latent block model (funLBM), which allows us to build summaries of these large consumption data through co-clustering. The funLBM model extends the usual latent block model to the functional case by assuming that the curves of one block live in a low-dimensional functional subspace. Thus, funLBM is able to model and cluster large data set with high-frequency curves. An SEM-Gibbs algorithm is proposed for model inference. An ICL criterion is also derived to address the problem of choosing the number of row and column groups. Numerical experiments on simulated and original Linky data show the usefulness of the proposed methodology.